Simulating a local emissions levy to fund local energy effiency retrofit
Focus on: Winchester
Ben Anderson
Last run at: 2021-10-20 14:48:10
1 About
Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
2 Citation
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom
3 Introduction
Background blurb about emissions, retofit, carbon tax/levy etc
4 Emissions Levy Case Study
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy value.
NB: no maps in the interests of speed
4.1 Data
We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
- CREDS place-based emissions estimates (contains data from a wide range of sources including https://epc.opendatacommunities.org/ for EPCs)
- 2018 residential gas & electricity use data
- Indices of Deprivation 2019
- Urban/Rural classification at LSOA level
- various LSOA look-up tables from geoportal
All analysis is at LSOA level. Cautions on inference from area level data apply.
4.2 CREDS place-based emissions estimates
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
- Emissions are presented as per capita…
- Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
## region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: South East 64 10435.31 3333.118Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 001B Wonston and Micheldever 1141 1221 693
## 2: Winchester 014C Denmead 1020 1112 731
## 3: Winchester 003A St Barnabas 896 896 442
## 4: Winchester 004E Alresford and Itchen Valley 844 876 534
## 5: Winchester 013D Southwick and Wickham 824 1304 901
## 6: Winchester 007A St Michael 814 1138 884## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 013D Southwick and Wickham 824 1304 901
## 2: Winchester 001B Wonston and Micheldever 1141 1221 693
## 3: Winchester 007A St Michael 814 1138 884
## 4: Winchester 014C Denmead 1020 1112 731
## 5: Winchester 014A Southwick and Wickham 726 977 744
## 6: Winchester 004A Upper Meon Valley 48 973 505## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 001B Wonston and Micheldever 1141 1221 693
## 2: Winchester 014C Denmead 1020 1112 731
## 3: Winchester 003A St Barnabas 896 896 442
## 4: Winchester 004E Alresford and Itchen Valley 844 876 534
## 5: Winchester 013D Southwick and Wickham 824 1304 901
## 6: Winchester 007A St Michael 814 1138 884Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
4.2.1 Estimate per dwelling emissions
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values| Name | …[] |
| Number of rows | 64 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 24933.52 | 8685.63 | 11275.19 | 18731.61 | 24005.72 | 31004.27 | 47031.62 | ▆▇▆▂▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2453.54 | 611.06 | 102.68 | 2169.00 | 2454.65 | 2840.90 | 3783.79 | ▁▁▅▇▂ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1132.35 | 254.86 | 778.32 | 956.46 | 1081.99 | 1238.38 | 1896.98 | ▇▇▂▂▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3585.90 | 662.13 | 1999.65 | 3107.95 | 3567.58 | 3977.05 | 5225.86 | ▂▅▇▅▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 243.91 | 374.48 | 17.96 | 52.53 | 90.85 | 222.03 | 2048.92 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3829.80 | 706.14 | 2416.16 | 3388.92 | 3855.76 | 4315.99 | 5354.20 | ▅▇▇▇▃ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 3023.43 | 930.43 | 1178.16 | 2275.68 | 3082.84 | 3832.39 | 4673.38 | ▅▇▇▇▇ |
| CREDSvan_kgco2e2018_pdw | 0 | 1 | 390.30 | 390.33 | 55.18 | 139.57 | 292.94 | 522.73 | 2093.34 | ▇▂▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 0 | 1 | 3413.73 | 1132.67 | 1252.96 | 2563.84 | 3379.91 | 4292.45 | 6447.09 | ▃▇▆▃▁ |
Examine patterns of per dwelling emissions for sense.
4.2.1.1 All emissions
Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -6.1744, df = 62, p-value = 5.634e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7492275 -0.4376306
## sample estimates:
## cor
## -0.6170604## LSOA11CD WD18NM All_Tco2e_per_dw
## Length:64 Length:64 Min. :11.28
## Class :character Class :character 1st Qu.:18.73
## Mode :character Mode :character Median :24.01
## Mean :24.93
## 3rd Qu.:31.00
## Max. :47.03## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01023241 Wonston and Micheldever 47.03162
## 2: E01023242 Badger Farm and Oliver's Battery 45.19596
## 3: E01023268 St Paul 44.31907
## 4: E01032859 Whiteley and Shedfield 38.08636
## 5: E01023270 St Paul 37.36082
## 6: E01023271 Whiteley and Shedfield 37.18580## LSOA11CD WD18NM All_Tco2e_per_dw
## 1: E01023285 Southwick and Wickham 12.45964
## 2: E01023256 St Bartholomew 11.79560
## 3: E01023252 St Michael 11.77504
## 4: E01023250 St Barnabas 11.64420
## 5: E01023221 Bishop's Waltham 11.30985
## 6: E01023261 St Luke 11.275194.2.1.2 Home energy use
Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 102.7 2169.0 2454.7 2453.5 2840.9 3783.8## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -5.42, df = 62, p-value = 1.036e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7133838 -0.3732082
## sample estimates:
## cor
## -0.5670019Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -0.96731, df = 62, p-value = 0.3371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.357040 0.127705
## sample estimates:
## cor
## -0.1219319Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -0.96731, df = 62, p-value = 0.3371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.357040 0.127705
## sample estimates:
## cor
## -0.1219319## RUC11 mean_gas_kgco2e mean_elec_kgco2e mean_other_energy_kgco2e
## 1: Rural town and fringe 2454.397 1090.922 183.53342
## 2: Rural village and dispersed 2276.440 1543.172 824.72984
## 3: Urban city and town 2521.518 1001.396 59.97038Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 5.8031, df = 62, p-value = 2.389e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4067955 0.7322961
## sample estimates:
## cor
## 0.5932803## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 5.8533, df = 62, p-value = 1.968e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4110607 0.7346623
## sample estimates:
## cor
## 0.5965891## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
How does the correlation look now?
4.2.1.3 Transport
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -4.9256, df = 62, p-value = 6.568e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6866312 -0.3271725
## sample estimates:
## cor
## -0.5303315## RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Rural town and fringe 3373.676 396.8821
## 2: Rural village and dispersed 3881.775 805.6082
## 3: Urban city and town 2453.895 225.0763Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.69702, df = 62, p-value = 0.4884
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1611247 0.3269007
## sample estimates:
## cor
## 0.088176934.2.2 Impute EPC counts
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 227.0 354.0 407.0 438.6 493.2 901.0## N elec meters## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 479.0 649.2 727.0 756.1 865.2 1304.0Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.3 Estimating the annual emissions levy
Case studies:
- Annual carbon tax
- Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
- September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind
- based on a Marginal Abatement Cost (MAC)
- 2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
4.2.3.1 Scenario 1: Central cost
The table below shows the overall £ GBP total for the case study area in £M.
## £m total## nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: 64 286.0057 28.69601 13.58922## £m by regions covered## region nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: South East 64 286.0057 28.69601 13.58922
The table below shows the mean per dwelling value rounded to the nearest £10.
## beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1: 6110 600 280
## beis_GBPtotal_c_energy_perdw
## 1: 880Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.7: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.8: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2762 4589 5881 6109 7596 11523## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw
## 1: E01023241 Winchester 003B Wonston and Micheldever 47031.62
## 2: E01023242 Winchester 009B Badger Farm and Oliver's Battery 45195.96
## 3: E01023268 Winchester 005D St Paul 44319.07
## 4: E01032859 Winchester 013F Whiteley and Shedfield 38086.36
## 5: E01023270 Winchester 005F St Paul 37360.82
## 6: E01023271 Winchester 013B Whiteley and Shedfield 37185.80
## beis_GBPtotal_c_perdw
## 1: 11522.747
## 2: 11073.010
## 3: 10858.171
## 4: 9331.159
## 5: 9153.402
## 6: 9110.522## LSOA11CD LSOA01NM WD18NM CREDStotal_kgco2e_pdw
## 1: E01023285 Winchester 013E Southwick and Wickham 12459.64
## 2: E01023256 Winchester 006C St Bartholomew 11795.60
## 3: E01023252 Winchester 007A St Michael 11775.04
## 4: E01023250 Winchester 005C St Barnabas 11644.20
## 5: E01023221 Winchester 012B Bishop's Waltham 11309.85
## 6: E01023261 Winchester 008B St Luke 11275.19
## beis_GBPtotal_c_perdw
## 1: 3052.612
## 2: 2889.921
## 3: 2884.886
## 4: 2852.829
## 5: 2770.912
## 6: 2762.421Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 25.16 531.41 601.39 601.12 696.02 927.03## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw
## 1: E01023230 Winchester 010D Badger Farm and Oliver's Battery 3.783790
## 2: E01023241 Winchester 003B Wonston and Micheldever 3.693281
## 3: E01023226 Winchester 010A Colden Common and Twyford 3.365778
## 4: E01023268 Winchester 005D St Paul 3.363035
## 5: E01023244 Winchester 009D Badger Farm and Oliver's Battery 3.308866
## 6: E01023266 Winchester 008D St Paul 3.211447
## beis_GBPtotal_c_gas_perdw
## 1: 927.0286
## 2: 904.8538
## 3: 824.6157
## 4: 823.9436
## 5: 810.6722
## 6: 786.8045## LSOA11CD LSOA01NM WD18NM gasTCO2e_pdw
## 1: E01023252 Winchester 007A St Michael 1.6836555
## 2: E01023251 Winchester 006A St Michael 1.6509760
## 3: E01023224 Winchester 014A Southwick and Wickham 1.5291709
## 4: E01023243 Winchester 009C Badger Farm and Oliver's Battery 1.4714619
## 5: E01023280 Winchester 004D Alresford and Itchen Valley 1.2586180
## 6: E01023225 Winchester 004A Upper Meon Valley 0.1026763
## beis_GBPtotal_c_gas_perdw
## 1: 412.49561
## 2: 404.48911
## 3: 374.64688
## 4: 360.50816
## 5: 308.36141
## 6: 25.15568Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 190.7 234.3 265.1 277.4 303.4 464.8## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw
## 1: E01023225 Winchester 004A Upper Meon Valley 1.896978
## 2: E01023236 Winchester 002A Alresford and Itchen Valley 1.774252
## 3: E01023229 Winchester 009A Badger Farm and Oliver's Battery 1.744728
## 4: E01023245 Winchester 013A Whiteley and Shedfield 1.642781
## 5: E01023280 Winchester 004D Alresford and Itchen Valley 1.641382
## 6: E01023274 Winchester 003E Wonston and Micheldever 1.561983
## beis_GBPtotal_c_elec_perdw
## 1: 464.7597
## 2: 434.6917
## 3: 427.4583
## 4: 402.4814
## 5: 402.1386
## 6: 382.6860## LSOA11CD LSOA01NM WD18NM elecTCO2e_pdw
## 1: E01023243 Winchester 009C Badger Farm and Oliver's Battery 0.8651011
## 2: E01023251 Winchester 006A St Michael 0.8545545
## 3: E01032860 Winchester 013G Whiteley and Shedfield 0.8455902
## 4: E01023237 Winchester 002B The Worthys 0.8438473
## 5: E01023261 Winchester 008B St Luke 0.7990677
## 6: E01023260 Winchester 008A St Luke 0.7783170
## beis_GBPtotal_c_elec_perdw
## 1: 211.9498
## 2: 209.3658
## 3: 207.1696
## 4: 206.7426
## 5: 195.7716
## 6: 190.6877Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 489.9 761.4 874.1 878.5 974.4 1280.34.2.3.2 Scenario 2: Rising block tariff
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw## 0% 25% 50% 75% 100%
## 11275.19 18731.61 24005.72 31004.27 47031.62## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw
## 1: 20.51384 2285.256 436.6463 0.0000
## 2: 11.30985 1379.801 0.0000 0.0000
## 3: 26.70112 2285.256 1292.1568 989.2112
## 4: 20.73296 2285.256 490.3313 0.0000
## 5: 20.88792 2285.256 528.2966 0.0000
## 6: 24.63309 2285.256 1292.1568 230.2465
## 7: 23.46564 2285.256 1159.8370 0.0000
## 8: 17.32122 2113.188 0.0000 0.0000
## 9: 24.73746 2285.256 1292.1568 268.5476
## 10: 31.63384 2285.256 1292.1568 2799.5188
## beis_GBPtotal_sc2_perdw
## 1: 2721.903
## 2: 1379.801
## 3: 4566.624
## 4: 2775.588
## 5: 2813.553
## 6: 3807.660
## 7: 3445.093
## 8: 2113.188
## 9: 3845.961
## 10: 6376.932| Name | …[] |
| Number of rows | 64 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 24.93 | 8.69 | 11.28 | 18.73 | 24.01 | 31.00 | 47.03 | ▆▇▆▂▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 4415.52 | 2638.62 | 1375.57 | 2300.11 | 3599.31 | 6145.88 | 12027.92 | ▇▃▂▂▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 3134190.38 | 1511997.02 | 817314.60 | 1823951.87 | 2962663.14 | 4118309.20 | 6086127.04 | ▇▆▆▃▅ |
## nLSOAs sum_total_sc1 sum_total_sc2
## 1: 64 286.0057 200.5882
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1: 2345.0893 264.6181
## 2: 1957.1116 238.7676
## 3: 2449.4413 264.6181
## 4: 2348.5987 264.6181
## 5: 1529.1709 186.5589
## 6: 102.6763 12.5265## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1: 2345.0893 264.6181 43.14158
## 2: 1957.1116 238.7676 0.00000
## 3: 2449.4413 264.6181 68.70784
## 4: 2348.5987 264.6181 44.00140
## 5: 1529.1709 186.5589 0.00000
## 6: 102.6763 12.5265 0.00000
## beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1: 0 307.7597
## 2: 0 238.7676
## 3: 0 333.3260
## 4: 0 308.6195
## 5: 0 186.5589
## 6: 0 12.5265## [1] 17.84687
## [1] 9.672869
## £m total## nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: 64 200.5882 17.84687 9.672869 114310## £m total by regions covered## region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: South East 64 200.5882 17.84687 9.672869 114310
4.2.4 Estimate retofit costs
- from A-E <- £13,300
- from F-G <- £26,800
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
## To retrofit D-E (£m)## [1] 317.2953## Number of dwellings: 23857## To retrofit F-G (£m)## [1] 50.83369## Number of dwellings: 1897## To retrofit D-G (£m)## [1] 368.129## To retrofit D-G (mean per dwelling)## [1] 14179.92## meanPerLSOA_GBPm total_GBPm
## 1: 5.752015 368.129## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.5 Compare levy with costs
4.2.5.1 Scenario 1
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.5.2 Scenario 2
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2.6 Years to pay…
4.2.6.1 Scenario 1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.209 1.887 2.442 2.632 3.097 5.120## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.15 14.31 16.12 16.75 18.33 35.11## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Highest retofit sum cost## LSOA11CD LSOA11NM WD18NM retrofitSum yearsToPay
## 1: E01023225 Winchester 004A Upper Meon Valley 13987309 35.11391
## 2: E01023236 Winchester 002A Alresford and Itchen Valley 10788912 15.18130
## 3: E01023245 Winchester 013A Whiteley and Shedfield 9834178 14.39001
## 4: E01023287 Winchester 001B Wonston and Micheldever 9399233 14.32065
## 5: E01023229 Winchester 009A Badger Farm and Oliver's Battery 9126369 13.33184
## 6: E01023284 Winchester 013D Southwick and Wickham 8734774 18.96992
## 7: E01023240 Winchester 003A St Barnabas 8118125 14.82935
## 8: E01023280 Winchester 004D Alresford and Itchen Valley 7835527 21.76513
## 9: E01023252 Winchester 007A St Michael 7522515 22.29159
## 10: E01023264 Winchester 007D St Michael 7336981 16.51768
## epc_D_pc epc_E_pc epc_F_pc epc_G_pc
## 1: 0.2673267 0.3267327 0.194059406 0.047524752
## 2: 0.3761468 0.2775229 0.094036697 0.034403670
## 3: 0.4256293 0.1647597 0.068649886 0.020594966
## 4: 0.4574315 0.1010101 0.008658009 0.001443001
## 5: 0.3725055 0.2439024 0.070953437 0.017738359
## 6: 0.2652608 0.1220866 0.046614872 0.011098779
## 7: 0.5180995 0.1312217 0.015837104 0.000000000
## 8: 0.3843844 0.2762763 0.093093093 0.033033033
## 9: 0.3190045 0.1233032 0.019230769 0.007918552
## 10: 0.3160377 0.2547170 0.047169811 0.011792453
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
4.2.6.2 Scenario 2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.179 2.341 4.057 4.472 5.950 10.282## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.15 14.31 16.12 16.75 18.33 35.11## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
What happens in Year 2 totally depends on the rate of upgrades…
4.2.6.3 Compare scenarios
Comparing pay-back times for the two scenarios - who does the rising block tariff help?
x = y line shown for clarity
5 R environment
5.1 R packages used
- base R (R Core Team 2016)
- bookdown (Xie 2016a)
- data.table (Dowle et al. 2015)
- ggplot2 (Wickham 2009)
- kableExtra (Zhu 2018)
- knitr (Xie 2016b)
- rmarkdown (Allaire et al. 2018)
- skimr (Arino de la Rubia et al. 2017)
5.2 Session info
6 Data Tables
I don’t know if this will work…
## Doesn't